Emily Zechman Berglund

Using Agent-Based Modeling for Water Resources Planning and Management

AbstractAgent-based systems have been developed for many scientific applications and simulation studies to model a group of actors and their interactions based on behavioral rules. Agent-based models and multiagent systems simulate the emergence of system-level properties based on the actions of adaptive agents that interact with other agents, react to environmental signals, and optimize decisions to achieve individual goals. In water resources planning and management, agent-based modeling has been applied to explore, simulate, and predict the performance of infrastructure design and policy decisions as they are influenced by human decision making, behaviors, and adaptations. The goal of this paper is to provide a comprehensive introduction to agent-based modeling for water resources researchers, students, and practitioners, and to explore water resources systems as complex adaptive systems that can be studied using agent-based modeling. Agent-based modeling is defined, and the characteristics of complex ...

Complex Adaptive Modeling Framework for Evaluating Adaptive Demand Management for Urban Water Resources Sustainability

AbstractNew water resources management methodologies are needed to address increasing demands and future uncertainty for urban water resources. Adaptive water demand management strategies provide an approach to improve the efficiency of water system operations and meet water demands by adapting flexibility to increasing stresses, such as droughts. This study simulates adaptive water demand management through the development of a complex adaptive system modeling framework, which couples cellular automata modeling, agent-based modeling, and hydrologic modeling to simulate land-use change, consumer behaviors, management decisions, the rainfall-runoff process, and reservoir storage. The model is applied to simulate the effect of demand management strategies on reductions in municipal water demands and on the sustained storage in a surface water supply reservoir. Historic and projected climate change hydroclimatic time series are used to assess the effectiveness of domestic water restrictions, including outdoo...

Cellular Automata Modeling Framework for Urban Water Reuse Planning and Management

AbstractWater reuse provides a sustainable approach to balance water supply and demand in urban areas, and reclaimed water can be used for nonpotable applications to reduce demands on freshwater sources. Construction of a secondary network is required to distribute reclaimed water. Implementing water reuse projects on a wider scale is challenged by the need for communitywide public acceptance and adoption, which may be a dynamic and adaptive process. The adoption of new water infrastructure can drive hydraulic conditions in both the reclaimed network and an existing drinking water network. This research develops a dynamic modeling framework using a cellular automata (CA) approach to simulate consumer adoption of reclaimed water. The framework couples the CA model of consumer adoption with water distribution system models of the drinking and reclaimed water systems. Emergent distribution system hydraulic conditions are simulated, and the capacity utilization and system performance are evaluated as consumer...

Parallel Evolutionary Algorithm for Designing Water Distribution Networks to Minimize Background Leakage

AbstractLeaks in water distribution systems waste energy and water resources, increase damage to infrastructure, and may allow contamination of potable water. This research develops an evolutionary algorithm-based approach to minimize the cost of water loss, new infrastructure, and operations that reduce background leakage. A new design approach is introduced that minimizes capital and operational costs, including energy and water loss costs. Design decisions identify a combination of infrastructure improvements, including pipe replacement and valve installment, and operation rules for tanks and pumps. Solution approaches are developed to solve both a single-objective and multiobjective problem formulation. A genetic algorithm and a nondominated sorting genetic algorithm are implemented within a high-performance computing platform to select tank sizes, pump placement and operations, placement of pressure-reducing valves, and pipe diameters for replacing pipes. The evolutionary algorithm approaches identif...

Real-Time Guidance for Hydrant Flushing Using Sensor-Hydrant Decision Trees

AbstractA utility may detect contaminant in a water distribution network through water quality sensor information, which indicates that a biological pathogen or chemical contaminant is present in the network. A utility manager should identify actions that can be taken to protect public health, and flushing a contaminant by opening a set of hydrants can be an effective response action. Hydrants should be selected and timed to flush the contaminant; however, accurately ascertaining the characteristics of the contaminant source may be impossible, which creates difficulties in developing a hydrant flushing strategy. This research develops a decision-making approach that is designed to select hydrant flushing strategies in response to sensor activations and does not require information about the characteristics of the contaminant source. A sensor-hydrant decision tree is introduced to provide a library of rules for opening and closing hydrants based on the order of activated sensors. Sensor-hydrant decision tr...

Agent-based modeling and evolutionary computation for disseminating public advisories about hazardous material emergencies

Abstract In the event of a large-scale disaster, an important aspect of humanitarian logistics is the distribution of information or warnings to the affected population. This research develops the problem formulation and solution approach for a specific routing for relief problem, in which warnings should be disseminated to an affected community, using public announcement systems mounted on emergency vehicles. The problem statement is formulated to maximize the number of individuals of a community who are protected. An evolutionary algorithm framework is developed by coupling an agent-based model with a variable-length genetic algorithm to route emergency vehicles. The dynamics of interactions among consumers, emergency vehicles, and the spatiotemporal trajectory of the hazard are simulated using an agent-based modeling approach, and a variable-length genetic algorithm approach selects routes to warn a maximum number of consumers before they are affected by the emergency. The example that is explored in this research is contamination of a water distribution network. A fleet of emergency vehicles is equipped with public address systems and is deployed to warn consumers to stop using contaminated water. The framework is demonstrated for an illustrative virtual city, Mesopolis. The results of the evolutionary algorithm framework are compared with two conventional routing optimization approaches, including a covering tour problem approach and a manual routing approach, for four contamination scenarios. The evolutionary algorithm can be applied to route emergency service vehicles to broadcast information for other emergencies, such as flash flooding, hazardous materials incidents, and severe weather.

Complex Adaptive Systems Framework to Simulate the Performance of Hydrant Flushing Rules and Broadcasts during a Water Distribution System Contamination Event

AbstractIn the event that a contaminant is introduced to a water distribution system, utility managers must respond quickly to protect public health. Mitigation strategies specify response actions,...

Scenario Analysis of Energy and Water Trade-Offs in the Expansion of a Dual Water System

AbstractUsing treated wastewater effluent (reclaimed water) for beneficial purposes can be a sustainable practice that reduces demand on potable networks. However, implementing reclaimed water networks can have unintended effects, specifically unintended increases in energy consumption. This case study employs multiperiod scenario analysis to examine energy consumption associated with the potable and reclaimed water systems for the Town of Cary, North Carolina. Using hydraulic planning models of both systems provided by the design engineers, the conveyance and additional treatment energy is tabulated. This method considers uncertainty in reclaimed water demand by varying the expected demand for each build out of the reclaimed water network. Differential electricity consumption is calculated as the difference between the electricity consumed to deliver reclaimed water through a secondary network compared to the electricity consumed to deliver the same volume through the potable water network. Demand uncert...

Agent-Based Modeling to Simulate Demand Management Strategies for Shared Groundwater Resources

Growing population centers in the arid southwest increase the demand for water, which is typically met through increased groundwater withdrawals. Hydro-climatic extremes due to climate change may also increase demands and decrease the replenishment of groundwater supply. Groundwater aquifers typically cross watershed, municipal, and management boundaries, and as a result, multiple diverse agencies manage a shared resource. Municipalities and management districts define individual demand management strategies that adapt water consumption to falling groundwater levels. The interactions among governing agencies, consumers, and the environment influence the performance of local management strategies and the availability of regional groundwater resources. This research develops an agent-based modeling (ABM) framework to analyze the dynamic interactions among changing water demands and limited groundwater resources under the stresses of population growth and climate change scenarios. Households are initialized as agents with properties and attributes to define indoor water use, outdoor water use, and water use reduction. Policy-maker agents are encoded to represent governing agencies that mandate or encourage water use restrictions. Demand management strategies are simulated as the response of a policy-maker agent to groundwater levels, safe yield, and climate variables. The framework is applied for municipalities located in the Verde River Basin, Arizona that withdraw groundwater from the Verde Formation-Basin Fill-Carbonate aquifer system. The effects of management strategies on water savings and basin-wide groundwater levels are explored, based on water use demands and reductions in different sectors of municipal water use. Insights gained through this simulation study can be used to guide groundwater policy-making under changing hydro-climatic scenarios for a long-term planning horizon.

An Empirical Acceptance-Resistance Agent-based Modeling Approach for Simulating the Adoption of Water Reuse

Though water reuse provides a promising and sustainable alternative for urban water supply, wide-scale implementation of water reuse within an existing water infrastructure system is challenged by the need for community-wide public acceptance and adoption. The public has historically perceived recycled water negatively, and, as a consequence, water reuse is typically omitted in the development of municipal water management plans. Consumer’s base acceptance and rejection of new technologies on an intuitive analysis of their risks and benefits, and their perceptions may change over time, based on interactions with other consumers, decision makers, and engineering infrastructure systems. This research creates a modeling framework to simulate the changing perceptions of consumers and their adoption of water reuse. The modeling framework is used to develop understanding about the mechanisms that drive the dynamic evolution of perceptions, which can aid the planning and decision making process for the integration of water reuse within existing water systems. This research develops an acceptance-resistance agent-based model to simulate the adoption and rejection of water reuse based on a “risk publics” framework, which is a theoretical model of how different groups perceive new technologies. The risk publics framework uses the perception of risk and benefits to determine the potential of households to adopt or resist new technology. Consumers are represented as agents, and their behaviors and attributes are developed using survey data of the US population, which measures attitudes, knowledge, and behavioral intentions for recycled water. The data is analyzed to determine empirical relationships among individuals; the presence of social groups; and informational and communicative variables governing individual use of reclaimed water. The variables are encoded in the agent-based modeling framework to simulate the key social mechanisms that impact consumer acceptance of water reuse. The framework couples the acceptance-resistance agent-based model of consumers, an agent-based model of utility management, and water distribution system models of the drinking and reclaimed water systems. The framework will be used to explore the interactions